Evoked hemodynamic response caused by an applied neurological stimulus and captured with fMRI is an indirect measure of neuronal activity that has become the work-horse of modern clinical neuroscience research on brain function. However, interpretation of the signal relative to the underlying molecular/cellular mechanisms responsible for the neurovascular coupling is an active area of investigation, and seemingly contradictory results continue to appear in the literature. Part of the problem is lack of noninvasive imaging options that directly assess local neural activity in the closed cranium, and development of new imaging approaches remains a significant challenge. As described in this project, we have identified a new possibility - functional neuro-poroelastography fNPE) which combines MRI acquisition of cerebrovascular pulsation in the brain with computational methods to estimate spatially localized mechanical and hydrodynamical brain tissue properties. fNPE captures mechano-functional responses of brain tissue and will provide the first spatial maps of its activity based on changes in mechanical properties, noninvasively and without exogenous head stimulation. fNPE is sensitive to multiscale mechanical networks of neural tissue, and thus, will reveal how sensory signals are linked to structural brain adaptation. This fundamentally new information can be added to neuro-computational models of electrical activation, metabolism and structure. We will develop fNPE with nonlinear inversion to yield 3D images of hydraulic conductivity, interstitial pressure and fluid fraction in addition to shear modulus. These results will be compared to a new wideband fMRE approach where images will also be formed through nonlinear inversion but with viscoelastic models. We will define the association of these new MRE methods with brain function using established stimulus protocols. The neuronal network not only transmits electrical signals within the brain, it also provides much of the mechanical scaffold which maintains structure and shape. The mobility of fluid controls the blood flow and ionic gradients required to trigger neuronal activity, but it also attenuates tissue motion and influences the arterial, venous and interstitial pressures within the cranium. Thus, we hypothesize that cerebrovascular flow and related tissue mechanical properties contribute to normal brain function and/or vice versa - brain function modulates cerebral hemodynamics, and concomitantly, brain tissue mechanics. Currently, limited knowledge and understanding exist on in vivo brain mechanics under normal and pathological conditions; yet, MRI methods specific to the mechanical and hydrodynamical properties of the brain, namely MRE techniques, are emerging and preliminary studies relating these properties to brain function are beginning to appear in the literature. Despite recent advances, the neurocomputational model inversion is under-developed in brain MRE, especially if fundamental advances in our understanding of the relationships between brain function and brain mechanical and hydrodynamical properties are to be elucidated. Neuro-computation in MRE, which includes fluid dynamics, poroelasticity and viscoelastic networks may open a new field of study within the framework of neuronal health (and function), which relates mechanical structure with brain tissue function. These developments will also inform models of neuro-degeneration given that the neuronal network is major contributor to the mechanical brain scaffold. This project will solidify an international collaboration in neuro-computational imaging that was recently begun. It will also accelerate realization of new imaging and computational methods for clinical neuroscience research on human brain function, as well as create a computational imaging framework that is applicable to other organ systems and diseases. The international exchange of ideas and expertise will strengthen the research base at the participating institutions and the knowledge of the investigators involved. Both institutions and research teams will have the neuro-computational inversion algorithms along with the MRI sequences required to deliver fNPE studies by the end of the proposed funding period. Graduate students and post-doctoral fellows will not only be trained in advanced MRI and computation methods, but they will also be exposed to and benefit from participating in a multi-disciplinary international collaboration by spending time-in-residenc at each institution.